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\title{Hybrid Orthogonal Genetic Algorithm for Global Numeric Optimization}
\author{Peter A. Stubberud and Matthew E. Jackson}
\email{stubber@ee.unlv.edu}
\institution{Department of Electrical and Computer Engineering, University of
Nevada - Las Vegas}
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\begin{document}
\maketitle

\part{Go to full-screen mode now by hitting CTRL-L}

\begin{slide}{Introduction to Blind Deconvolution}
\slidetextsize
\begin{itemize}
\item Blind deconvolution fundamental in signal processing

\item Observation, $\mb x = \{x(t),\,t\in\mc T\}$, modelled as the convolution of unknown
source, $\{s(t),\,t\in\mc T\}$, with unknown distortion operator, $\mc A$; \ie\ $x(t)
\defas s(t) \conv\ \mc A$
\begin{figure}[t]
\begin{center}
        \includegraphics[width=\textwidth]{make_fig_final}
\end{center}
\end{figure}
\item Estimate $\mc A$, \emph{or} $\hat s(t) = a\,s(t - \tau)$, a scaled shifted version
of $s(t)$, where $a,\,\tau\in\real{}$, given only the observations, $\mb x$
\end{itemize}
\end{slide}

\begin{slide}{Equation Sheet 1}
\slidetextsize

 \begin{equation*}
  \text{STF}(z)=\frac{F(z)G(z)}{1+z^{-1}G(z)H(z)}\quad
  \text{NTF}=\frac{1}{1+z^{-1}G(z)H(z)}
 \end{equation*}
\end{slide}


\end{document}
